Study on Diesel Engine Fault Diagnosis Method based on Integration Super Parent One Dependence Estimator

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Wang Xin 1,* Yu Hongliang 1 Zhang Lin 2 Huang Chaoming 1 Song Yuchao 1

1. College of Marine Engineering, Dalian Maritime University, Dalian, China

2. School of Textile and Light Industry, Dalian Polytechnic University, Dalian, China

* Corresponding author.


Received: 3 Nov. 2010 / Revised: 26 Nov. 2010 / Accepted: 6 Jan. 2011 / Published: 8 Feb. 2011

Index Terms

Diesel engine, naïve Bayesian classifier, fault diagnosis, one-dependence classifier


Under the background of the deficiencies and shortcomings in traditional diesel engine fault diagnostic, the naïve Bayesian classifier method which built on the basis of the probability density function is adopted to diagnose the fault of diesel engine. A new approach is proposed to weight the super-parent one dependence estimators. To verify the validity of the proposed method, the experiments are performed using 16 datasets collected by University of California Irvine (UCI) and 5 diesel engine datasets collected by our lab. The comparison experimental results with other algorithms demonstrate the effectiveness of the proposed method.

Cite This Paper

Wang Xin,Yu Hongliang,Zhang Lin,Huang Chaoming,Song Yuchao, "Study on Diesel Engine Fault Diagnosis Method based on Integration Super Parent One Dependence Estimator", IJIGSP, vol.3, no.1, pp.10-16, 2011. DOI: 10.5815/ijigsp.2011.01.02


[1]Sahami M. Learning limited dependence Bayesian classifiers. Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 334–338, 1996. 

[2]E. Keogh, M. Pazzani. Learning Augmented Bayesian Classifiers: A Comparison of Distribution-Based and Classification-Based Approaches. Proc. Int’l Workshop Artificial Intelligence and Statistics, pp. 225-230, 1999. 

[3]N. Friedman, D. Geiger, M. Goldszmidt. Bayesian Network Classifiers. Machine Learning, vol. 29, pp. 131-163, 1997. 

[4]Jiang, L., Zhang, H., Cai, Z., Su, J. One Dependence Augmented Naive Bayes. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp.186–194. Springer, Heidelberg , 2005. 

[5]G.I. Webb, J. Boughton, Z. Wang. Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning, vol. 58, pp. 5-24, 2005. 

[6]Jiang L., Zhang H. Weightily averaged one-dependence estimators. In: Yang Q.,Webb G.(eds.) PRICAI2006.LNCS(LNAI), vol.4099, pp970-974, Springer, Heidelberg, 2006. 

[7]Liangxiao Jiang, Harry Zhang, and Zhihua Cai. A Novel Bayes Model: Hidden Naïve Bayes. IEEE Transactions on knowledge and data engineering, VOL. 21, NO.10, October 2009. 

[8]Flores MJ, Gamez JA, Martinez AM, Puerta JM. HODE: Hidden One-Dependence Estimator. 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 5590, 481-492, Verona, ITALY, JUL 01-03, 2009. 

[9]Yang, Y., Korb, K., Ting, K.-M., Webb, G.I.: Ensemble Selection for SuperParent-One-Dependence Estimators. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS, vol. 3809, pp. 102–112. Springer, Heidelberg , 2005. 

[10]Li N, Jiang Y, Zhou Z. H. Model-Likelihood Based SuperParent-One-Dependence Estimator Ensemble Method. PR&AI. Vol20, No6, dec 2007.